Similarity estimation between concepts in a knowledge base is a hard problem because of coverage (not all terms in the ontology are usually in the corpus). Improving similarity estimation between concepts is crucial to implement Question/Answering QA analytics for answer scoring. Existing solutions are based on graph algorithms (distance between nodes into graphs derived by the ontology) or corpus based algorithms (distributional or contextual similarity between terms associated to concepts in the ontology). Both solutions are viable but limited because the former does not take into account distributional properties of concepts such as their frequency, while the latter has not enough coverage (not all terms in the ontology are actually in the corpus) and word sense disambiguation is not easy.